Is running more epochs really a direct cause of overfitting?
The Problem of Overfitting
In most cases, when you increase a lot the number of epochs your model finally overfits. This is because your model reaches the point that it does not learn anymore but tries to remember what it has seen before. This is overfitting. So there is often a trade-off between the number of epochs and overfitting. In general a good way to avoid overfitting, except for fine-tuning, regularization, dropout etc, is to understand what you have from the learning curve. In most cases overfitting happens, after some epochs have passed, and, as a result the training error still decreases, whereas the validation error flactuates or increases. If so, you should save only the learning updates before overfitting appears and/or validation error is minimum.
Methods: early-stopping, checkpoint.
useful link: https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/